#!/usr/bin/python
# -*- coding:utf-8 -*-
# @FileName : DL4_test5.py
# Author    : myh

import math
import torch
from torch import nn
from d2l import torch as d2l
import numpy as np


def init_params():
    w = torch.normal(0, 1, size=(num_inputs, 1), requires_grad=True)
    b = torch.zeros(1, requires_grad=True)
    return [w, b]

def l2_penalty(w):
    return torch.sum(w.pow(2)) / 2

def l1_penalty(w):
    return torch.sum(abs(w))

def evaluate_loss(net, data_iter, loss):  #@save
    """评估给定数据集上模型的损失"""
    metric = d2l.Accumulator(2)  # 损失的总和,样本数量
    for X, y in data_iter:
        out = net(X)
        y = y.reshape(out.shape)
        l = loss(out, y)
        metric.add(l.sum(), l.numel())
    return metric[0] / metric[1]

def train(lambd):
    w, b = init_params()
    net, loss = lambda X: d2l.linreg(X, w, b), d2l.squared_loss
    num_epochs, lr = 100, 0.003
    # animator = d2l.Animator(xlabel='epochs', ylabel='loss', yscale='log',
    #                         xlim=[5, num_epochs], legend=['train', 'test'])
    for epoch in range(num_epochs):
        for X, y in train_iter:
            # 增加了L2范数惩罚项，
            # 广播机制使l2_penalty(w)成为一个长度为batch_size的向量
            l = loss(net(X), y) + lambd * l1_penalty(w)
            l.sum().backward()
            d2l.sgd([w, b], lr, batch_size)
        # if (epoch + 1) % 5 == 0:
        #     animator.add(epoch + 1, (d2l.evaluate_loss(net, train_iter, loss),
        #                              d2l.evaluate_loss(net, test_iter, loss)))
    print('w的L2范数是：', torch.norm(w).item())
    return evaluate_loss(net, train_iter, loss),evaluate_loss(net, test_iter, loss)


if __name__ == '__main__':
    n_train, n_test, num_inputs, batch_size = 20, 100, 200, 5
    true_w, true_b = torch.ones((num_inputs, 1)) * 0.01, 0.05
    train_data = d2l.synthetic_data(true_w, true_b, n_train)
    train_iter = d2l.load_array(train_data, batch_size)
    test_data = d2l.synthetic_data(true_w, true_b, n_test)
    test_iter = d2l.load_array(test_data, batch_size, is_train=False)

    max_lambda =10
    animator = d2l.Animator(xlabel='lambda', ylabel='loss', yscale='log' ,xlim=[1, max_lambda], legend=['train','test'])
    for  lambda_val in range(0,max_lambda+1):
        animator.add(lambda_val,(train(lambd=lambda_val)))

    d2l.plt.show()